Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification

• URL: http://arxiv.org/abs/2503.07396v1
• Date: Mon, 10 Mar 2025 14:42:51 GMT
• Title: Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification
• Authors: Kexin Di, Xiuxing Li, Yuyang Han, Ziyu Li, Qing Li, Xia Wu,
• Abstract summary: Existing methods aim to tackle this problem by locating and aligning relevant local features.<n>High intra-class variability in real-world images poses significant challenges in locating semantically relevant local regions under few-shot settings.<n>We propose the generalization-optimized Systems Consolidation Adaptive Memory Dual-Network, SCAM-Net.
• Score: 10.824399627455326
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Few-shot image classification has become a popular research topic for its wide application in real-world scenarios, however the problem of supervision collapse induced by single image-level annotation remains a major challenge. Existing methods aim to tackle this problem by locating and aligning relevant local features. However, the high intra-class variability in real-world images poses significant challenges in locating semantically relevant local regions under few-shot settings. Drawing inspiration from the human's complementary learning system, which excels at rapidly capturing and integrating semantic features from limited examples, we propose the generalization-optimized Systems Consolidation Adaptive Memory Dual-Network, SCAM-Net. This approach simulates the systems consolidation of complementary learning system with an adaptive memory module, which successfully addresses the difficulty of identifying meaningful features in few-shot scenarios. Specifically, we construct a Hippocampus-Neocortex dual-network that consolidates structured representation of each category, the structured representation is then stored and adaptively regulated following the generalization optimization principle in a long-term memory inside Neocortex. Extensive experiments on benchmark datasets show that the proposed model has achieved state-of-the-art performance.

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